Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
156 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

IDRNet: Intervention-Driven Relation Network for Semantic Segmentation (2310.10755v1)

Published 16 Oct 2023 in cs.CV

Abstract: Co-occurrent visual patterns suggest that pixel relation modeling facilitates dense prediction tasks, which inspires the development of numerous context modeling paradigms, \emph{e.g.}, multi-scale-driven and similarity-driven context schemes. Despite the impressive results, these existing paradigms often suffer from inadequate or ineffective contextual information aggregation due to reliance on large amounts of predetermined priors. To alleviate the issues, we propose a novel \textbf{I}ntervention-\textbf{D}riven \textbf{R}elation \textbf{Net}work (\textbf{IDRNet}), which leverages a deletion diagnostics procedure to guide the modeling of contextual relations among different pixels. Specifically, we first group pixel-level representations into semantic-level representations with the guidance of pseudo labels and further improve the distinguishability of the grouped representations with a feature enhancement module. Next, a deletion diagnostics procedure is conducted to model relations of these semantic-level representations via perceiving the network outputs and the extracted relations are utilized to guide the semantic-level representations to interact with each other. Finally, the interacted representations are utilized to augment original pixel-level representations for final predictions. Extensive experiments are conducted to validate the effectiveness of IDRNet quantitatively and qualitatively. Notably, our intervention-driven context scheme brings consistent performance improvements to state-of-the-art segmentation frameworks and achieves competitive results on popular benchmark datasets, including ADE20K, COCO-Stuff, PASCAL-Context, LIP, and Cityscapes. Code is available at \url{https://github.com/SegmentationBLWX/sssegmentation}.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (89)
  1. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587, 2017a.
  2. Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2881–2890, 2017.
  3. Unified perceptual parsing for scene understanding. In Proceedings of the European conference on computer vision (ECCV), pages 418–434, 2018.
  4. Non-local neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7794–7803, 2018.
  5. Asymmetric non-local neural networks for semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 593–602, 2019a.
  6. Object-contextual representations for semantic segmentation. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VI 16, pages 173–190. Springer, 2020a.
  7. Scene parsing through ade20k dataset. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 633–641, 2017.
  8. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213–3223, 2016.
  9. Deep learning. nature, 521(7553):436–444, 2015.
  10. Attention is all you need. Advances in neural information processing systems, 30, 2017.
  11. Masked-attention mask transformer for universal image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1290–1299, 2022.
  12. Segnext: Rethinking convolutional attention design for semantic segmentation. arXiv preprint arXiv:2209.08575, 2022.
  13. Mining contextual information beyond image for semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7231–7241, 2021a.
  14. Isnet: Integrate image-level and semantic-level context for semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7189–7198, 2021b.
  15. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012–10022, 2021.
  16. Internimage: Exploring large-scale vision foundation models with deformable convolutions. arXiv preprint arXiv:2211.05778, 2022a.
  17. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6881–6890, 2021.
  18. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3431–3440, 2015.
  19. Object categorization using co-occurrence, location and appearance. In 2008 IEEE Conference on Computer Vision and Pattern Recognition, pages 1–8. IEEE, 2008.
  20. Context-based vision system for place and object recognition. In Computer Vision, IEEE International Conference on, volume 2, pages 273–273. IEEE Computer Society, 2003.
  21. Mcibi++: Soft mining contextual information beyond image for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.
  22. Gcnet: Non-local networks meet squeeze-excitation networks and beyond. In Proceedings of the IEEE/CVF international conference on computer vision workshops, pages 0–0, 2019.
  23. Disentangled non-local neural networks. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XV 16, pages 191–207. Springer, 2020.
  24. Deformable convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 764–773, 2017.
  25. Deformable convnets v2: More deformable, better results. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9308–9316, 2019b.
  26. R Dennis Cook. Detection of influential observation in linear regression. Technometrics, 19(1):15–18, 1977.
  27. Ross Girshick. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 1440–1448, 2015.
  28. Res2net: A new multi-scale backbone architecture. IEEE transactions on pattern analysis and machine intelligence, 43(2):652–662, 2019.
  29. Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence, 43(10):3349–3364, 2020.
  30. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
  31. Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1492–1500, 2017.
  32. Fasterseg: Searching for faster real-time semantic segmentation. arXiv preprint arXiv:1912.10917, 2019a.
  33. Learning dynamic routing for semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8553–8562, 2020.
  34. Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 82–92, 2019.
  35. Beit: Bert pre-training of image transformers. arXiv preprint arXiv:2106.08254, 2021.
  36. Twins: Revisiting the design of spatial attention in vision transformers. Advances in Neural Information Processing Systems, 34:9355–9366, 2021.
  37. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.
  38. Beit v2: Masked image modeling with vector-quantized visual tokenizers. arXiv preprint arXiv:2208.06366, 2022.
  39. Segmenter: Transformer for semantic segmentation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7262–7272, 2021.
  40. Segformer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34:12077–12090, 2021.
  41. Metaformer is actually what you need for vision. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10819–10829, 2022.
  42. Adaptive pyramid context network for semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7519–7528, 2019.
  43. Ccnet: Criss-cross attention for semantic segmentation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 603–612, 2019a.
  44. Context prior for scene segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12416–12425, 2020.
  45. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4):834–848, 2017b.
  46. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European conference on computer vision (ECCV), pages 801–818, 2018.
  47. Denseaspp for semantic segmentation in street scenes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3684–3692, 2018.
  48. Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122, 2015.
  49. The lovász-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4413–4421, 2018.
  50. Adaptive affinity fields for semantic segmentation. In Proceedings of the European conference on computer vision (ECCV), pages 587–602, 2018.
  51. Region mutual information loss for semantic segmentation. Advances in Neural Information Processing Systems, 32, 2019.
  52. Exploring cross-image pixel contrast for semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7303–7313, 2021.
  53. Contrastive learning for label efficient semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10623–10633, 2021.
  54. Per-pixel classification is not all you need for semantic segmentation. Advances in Neural Information Processing Systems, 34:17864–17875, 2021.
  55. K-net: Towards unified image segmentation. Advances in Neural Information Processing Systems, 34:10326–10338, 2021.
  56. Semantic segmentation with boundary neural fields. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3602–3610, 2016.
  57. Boundary-aware feature propagation for scene segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6819–6829, 2019.
  58. Devil in the details: Towards accurate single and multiple human parsing. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 4814–4821, 2019.
  59. Active boundary loss for semantic segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 2397–2405, 2022b.
  60. Segfix: Model-agnostic boundary refinement for segmentation. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XII 16, pages 489–506. Springer, 2020b.
  61. Interlaced sparse self-attention for semantic segmentation. arXiv preprint arXiv:1907.12273, 2019b.
  62. Object detection with discriminatively trained part-based models. IEEE transactions on pattern analysis and machine intelligence, 32(9):1627–1645, 2009.
  63. A tree-based context model for object recognition. IEEE transactions on pattern analysis and machine intelligence, 34(2):240–252, 2011.
  64. Context based object categorization: A critical survey. Computer vision and image understanding, 114(6):712–722, 2010.
  65. The role of context for object detection and semantic segmentation in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 891–898, 2014.
  66. Relation networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3588–3597, 2018.
  67. Spatial-aware graph relation network for large-scale object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9298–9307, 2019.
  68. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.
  69. Spatial memory for context reasoning in object detection. In Proceedings of the IEEE international conference on computer vision, pages 4086–4096, 2017.
  70. Francesco Mezzadri. How to generate random matrices from the classical compact groups. arXiv preprint math-ph/0609050, 2006.
  71. A theoretical framework for back-propagation. In Proceedings of the 1988 connectionist models summer school, volume 1, pages 21–28, 1988.
  72. Learning representations by back-propagating errors. nature, 323(6088):533–536, 1986.
  73. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1):1929–1958, 2014.
  74. Coco-stuff: Thing and stuff classes in context. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1209–1218, 2018.
  75. Look into person: Self-supervised structure-sensitive learning and a new benchmark for human parsing. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 932–940, 2017.
  76. The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2):303–338, 2010.
  77. Automatic differentiation in pytorch. 2017.
  78. Zhenchao Jin. Sssegmenation: An open source supervised semantic segmentation toolbox based on pytorch. arXiv preprint arXiv:2305.17091, 2023.
  79. Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3):211–252, 2015.
  80. Guided curriculum model adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7374–7383, 2019.
  81. Dark model adaptation: Semantic image segmentation from daytime to nighttime. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pages 3819–3824. IEEE, 2018.
  82. Instance-level human parsing via part grouping network. In Proceedings of the European conference on computer vision (ECCV), pages 770–785, 2018.
  83. Expectation-maximization attention networks for semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9167–9176, 2019.
  84. Ocnet: Object context for semantic segmentation. International Journal of Computer Vision, 129(8):2375–2398, 2021.
  85. Acnet: Attention based network to exploit complementary features for rgbd semantic segmentation. In 2019 IEEE International Conference on Image Processing (ICIP), pages 1440–1444. IEEE, 2019.
  86. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015.
  87. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961–2969, 2017.
  88. Microsoft coco: Common objects in context. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740–755. Springer, 2014.
  89. Mmdetection: Open mmlab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155, 2019b.
Citations (10)
View on Semantic Scholar

Summary

  • The paper introduces a novel intervention-driven relation network that transforms pixel-level relations into semantic-level representations using deletion diagnostics.
  • It enhances contextual feature aggregation by grouping pixels with pseudo labels and applying feature enhancement modules to boost discriminability.
  • Experimental results show that IDRNet improves mIoU by over 4% on benchmarks like ADE20K and Cityscapes across various segmentation architectures.

Intervention-Driven Relation Network for Semantic Segmentation: An Overview

The paper "IDRNet: Intervention-Driven Relation Network for Semantic Segmentation" introduces a novel approach to modeling contextual relations in semantic segmentation tasks. The proposed Intervention-Driven Relation Network (IDRNet) aims to enhance the aggregation of contextual information among pixels, addressing the limitations of existing multi-scale-driven and similarity-driven paradigms.

Core Contributions

The authors propose a paradigm that employs deletion diagnostics to guide the modeling of pixel relations. This approach effectively transforms pixel-level relations into semantic-level relations, thereby simplifying the task while preserving crucial contextual information. The paper delineates the process into several stages:

  1. Semantic-Level Representation Generation: Pixel-level representations are grouped into semantic-level representations using pseudo labels, allowing for more efficient relation modeling.
  2. Feature Enhancement: A module is employed to increase the distinguishing capacity of semantic-level representations, using either orthogonal matrices or dataset-level representations.
  3. Feature Interaction: A relation matrix, updated through deletion diagnostics, guides the interaction between semantic-level representations, enhancing their contextual information.
  4. Final Prediction: The interacted representations are fused into the pixel-level features, leading to improved prediction accuracy.

Numerical Results

The experimental results illustrate the effectiveness of IDRNet, showcasing consistent performance improvements across multiple state-of-the-art segmentation frameworks such as FCN, PSPNet, DeeplabV3, and UPerNet. IDRNet achieves competitive results on well-known datasets like ADE20K, COCO-Stuff, PASCAL-Context, LIP, and Cityscapes. For instance, IDRNet integrated with a basic FCN framework outperforms traditional context schemes, improving mIoU by over 4% on several datasets.

Implications and Future Directions

This research holds substantial implications for semantic segmentation, particularly in enhancing the adaptability and efficacy of context modeling. By simplifying the problem to semantic-level relation modeling, IDRNet offers a scalable solution that can adapt to various vision tasks such as object detection and instance segmentation. The introduction of deletion diagnostics presents a shift from conventional methods reliant on predetermined priors, thus potentially inspiring new methodologies in other AI fields.

Future research could explore extending the intervention-driven paradigm to other domains, further validating its generality and robustness. Additionally, integrating the approach with other novel architectures, such as transformers, could provide insights into hybrid models that leverage the strengths of both paradigms.

Conclusion

"IDRNet: Intervention-Driven Relation Network for Semantic Segmentation" marks a significant step towards refining context aggregation in dense prediction tasks. The intervention-driven framework, guided by deletion diagnostics, advances the paradigm of pixel relation modeling, offering a path to more effective and generalized solutions in semantic segmentation and beyond.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets